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Enregistrement W4240764292 · doi:10.2118/2007-134

Potential For Alkali-Surfactant Flooding In Heavy Oil Reservoirs Through Oil-in-Water Emulsification

2007· article· en· W4240764292 sur OpenAlexafffundabout
J. Bryan, Apostolos Kantzas

Notice bibliographique

RevueCanadian International Petroleum Conference · 2007
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensUniversity of Calgary
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
Mots-clésWater floodingPulmonary surfactantPetroleum engineeringAlkali metalFlooding (psychology)Environmental scienceGeologyChemical engineeringChemistryEngineering

Résumé

récupéré en direct d'OpenAlex

Abstract Alkali-surfactant flooding is an established enhanced oil recovery technique in conventional oil reservoirs, whereby the injected chemical reduces the oil/water interfacial tension, leading to less trapping of oil ganglia. In the past, there have been some studies of alkali and alkali-surfactant flooding of heavy oil systems as well, and it has been observed that chemical injection can lead to improved oil recovery. The heavy oil recovery mechanism proposed in this work is the creation of oil-in-water emulsions, which may form under conditions of low interfacial tension and shear due to flow through rock pores. Oil may either be produced in the water (emulsification & entrainment) or the droplets may plug the rock pores, leading to improved sweep efficiency (emulsification & entrapment). Both of these mechanisms are investigated in laboratory systems of varying rock permeability, using a heavy oil with a viscosity of 11 000 mPas. When oil-inwater emulsions form, the oil recovery can be improved significantly, even without the addition of polymer for mobility control. The effect of permeability and varying injection rates are considered, to understand how different ranges of shear affect the efficiency of these emulsion systems. Introduction Several countries in the world, notably Canada and Venezuela, contain significant deposits of heavy oil and bitumen. As Canadian conventional oil reserves continue to decline, the industry interest is now shifting rapidly towards the recovery of this unconventional crude. While the immensity of this resource base is exciting, heavy oil reservoirs pose unique challenges when designing recovery strategies. The Canadian oil sands are unconsolidated, high porosity and high permeability reservoirs. Ease of flow is therefore not an issue, as it is in many conventional oil reservoirs. Rather, the single biggest impediment to the successful recovery of heavy oil and bitumen is the high oil viscosity. Heavy oil reservoirs are a special subset of our oil sands, whereby the oil viscosity at reservoir temperature varies from around 50 mPas up to around 50 000 mPas. At reservoir conditions the oil requires high pressure draw downs in order for it to flow even through the permeable sands, after which point the reservoir has been depleted of all of its natural energy. In order to recover additional heavy oil, energy has to be injected into the reservoir. Often, this takes the form of a fluid that displaces the oil, meaning that the oil must be made to flow to production wells. Most improved/enhanced oil recovery schemes focus on reduction of the oil viscosity through the application of heat or miscible solvents. However, many of the Canadian heavy oil reservoirs are relatively small and thin, and have been disturbed to an unknown extent during primary production. Therefore, the development of injection strategies that are inexpensive (i.e. non-thermal) and easy to control will be of considerable benefit to heavy oil producers. In this work, alkali-surfactant solution is investigated as a potential non-thermal injection fluid. It is demonstrated that through the injection of low concentrations of alkali and preformed surfactant solution, oil recovery can be increased significantly above that of waterflooding.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,363
Score d'incertitude au seuil0,987

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,252
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2007
Routes d'admission3
Résumé présentoui

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